Electrocardiogram (ECG) signals as biometrics in securing Wireless Body Area Network

Ramli, Sofia Najwa; Ahmad, Rabiah; Abdollah, Mohd Faizal

doi:10.1109/icitst.2013.6750259

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…They used hybrid authentication model as a conceptual framework for the system. In addition, Ramli et al published another study [15] in the same year that could be an example for this study. They use ECG signals as biometric data for secure WBAN technology.…”

Section: Related Workmentioning

confidence: 95%

Security Analysis of Medical Devices within Wireless Body Area Networks and Mobile Health Applications

DEMİR¹,

Tatli²

2018

Bilişim Teknolojileri Dergisi

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Body Area Networks (BAN) consisting of medical devices that interact with human body have been in recent years developed in accordance with technical developments in healthcare area. Thus, devices automatically measuring human blood pressure or insulin value and transmitting the measured data into hospital management systems have been started to be used in daily-life. These medical devices aim to improve health of patients, however they are also subjected to unauthorized access and manipulation which creates substantial risk for the patients. In this paper, the analysis results of medical devices used in Wireless Body Area Networks (WBAN) and mobile health applications are explained according to different security requirements and aspects. All threats and risks faced by remotely accessible medical devices in WBAN are identified by applying WBAN threat modeling. Performed security analysis and penetration testing of mobile health applications running on smart devices are presented in detail. In addition, secure architecture principles for WBAN designs are discussed in detail.

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Section: Related Workmentioning

confidence: 95%

Security Analysis of Medical Devices within Wireless Body Area Networks and Mobile Health Applications

DEMİR¹,

Tatli²

2018

Bilişim Teknolojileri Dergisi

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“…A few randomness extractor algorithms from IPI have been proposed in the literature. Proposed methods are including using XOR function [26], gray-coding [27] and using frequency domain [28], [24], [29]. In some studies, a combination of algorithms is used for randomness extraction.…”

Section: Related Workmentioning

confidence: 99%

“…Several extracting methods did not perform any randomness test to examine the quality of proposed algorithm (e.g. [34], [35], [28], [24], [36], [37], [29]). In these works the aim of the paper was not examining the randomness of proposed extraction method.…”

Section: Related Workmentioning

confidence: 99%

Extracting Randomness from the Trend of IPI for Cryptographic Operations in Implantable Medical Devices

Chizari

Lupu

2021

IEEE Trans. Dependable and Secure Comput.

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Achieving secure communication between an Implantable Medical Device (IMD) inside the body and a gateway outside the body has showed its criticality with recent reports of hackings such as in St. Jude Medical's Implantable Cardiac Devices, Johnson and Johnson insulin pumps and vulnerabilities in brain Neuro-implants. The use of asymmetric cryptography in particular is not a practical solution for IMDs due to the scarce computational and power resources, symmetric key cryptography is preferred. One of the factors in security of a symmetric cryptographic system is to use a strong key for encryption. A solution to develop such a strong key without using extensive resources in an IMD, is to extract it from the body physiological signals. In order to have a strong enough key, the physiological signal must be a strong source of randomness and InterPulse Interval (IPI) has been advised to be such that. A strong randomness source should have five conditions: Universality (available on all people), Liveness (available at any-time), Robustness (strong random number), Permanence (independent from its history) and Uniqueness (independent from other sources). Nevertheless, for current proposed random extraction methods from IPI these conditions (mainly last three conditions) were not examined. In this study, firstly, we proposed a methodology to measure the last three conditions: Information secrecy measures for Robustness, Santha-Vazirani Source delta value for Permanence and random sources dependency analysis for Uniqueness. Then, using a huge dataset of IPI values (almost 900,000,000 IPIs), we showed that IPI does not have conditions of Robustness and Permanence as a randomness source. Thus, extraction of a strong uniform random number from IPI value, mathematically, is impossible. Thirdly, rather than using the value of IPI, we proposed the trend of IPI as a source for a new randomness extraction method named as Martingale Randomness Extraction from IPI (MRE-IPI). We evaluated MRE-IPI and showed that it satisfies the Robustness condition completely and Permanence to some level. Finally, we used NIST STS and Dieharder test suites and showed that MRE-IPI is able to outperform all recent randomness extraction methods from IPIs and its quality is half of the AES random number. MRE-IPI, still, is not a strong random number and could not be used as the secret key for a secure communication, however, it can be used as a one-time pad in exchanging the secret key for a communication. In this case, the usage of MRE-IPI will be kept at a minimum level and reduces the probability of breaking it. To the best of our knowledge, this is the first work in this area which uses such a comprehensive method and large dataset to examine the randomness of a physiological signal.

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“…Several randomness extractor algorithms from IPI have been proposed in the literature. Proposed methods are including using XOR function [11], gray-coding [12] and using frequency domain [13,14,15]. In some studies a combinations of algorithms are used for randomness extraction.…”

Section: Related Workmentioning

confidence: 99%

“…There are several physiological randomness extraction methods which did not perform any randomness test to examine the quality of proposed algorithm (e.g. [20,21,13,14,22,23,15]). To evaluate the quality of a randomness extractor, there are two aspects to consider: the dataset and the methodology of evaluation.…”

Section: Related Workmentioning

confidence: 99%

Randomness of physiological signals in generation cryptographic key for secure communication between implantable medical devices inside the body and the outside world

Chizari¹,

Lupu²,

Thomas³

2018

Living in the Internet of Things: Cybersecurity of the IoT - 2018

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Electrocardiogram (ECG) signals as biometrics in securing Wireless Body Area Network

Cited by 10 publications

References 12 publications

Security Analysis of Medical Devices within Wireless Body Area Networks and Mobile Health Applications

Security Analysis of Medical Devices within Wireless Body Area Networks and Mobile Health Applications

Extracting Randomness from the Trend of IPI for Cryptographic Operations in Implantable Medical Devices

Randomness of physiological signals in generation cryptographic key for secure communication between implantable medical devices inside the body and the outside world

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